one noise variable, logistic regression

## [1] "*************************************************************"
## [1] "one noise variable, logistic regression"
## [1] "bSigmaBest 39"
## [1] "naive effects model"
## [1] "one noise variable, logistic regression naive effects model fit model:"
## 
## Call:
## glm(formula = formulaL, family = binomial, data = trainData)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.8068  -1.0493   0.5770   0.9415   2.5190  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  0.18447    0.05074   3.635 0.000277 ***
## n1           2.20269    0.13545  16.262  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 2772.6  on 1999  degrees of freedom
## Residual deviance: 2256.7  on 1998  degrees of freedom
## AIC: 2260.7
## 
## Number of Fisher Scoring iterations: 6
## 
## [1] "one noise variable, logistic regression naive effects model train mean deviance 1.62786601580457"

## [1] "one noise variable, logistic regression naive effects model test mean deviance 3.71500787962648"

## [1] "effects model, sigma= 39"
## [1] "one noise variable, logistic regression effects model, sigma= 39 fit model:"
## 
## Call:
## glm(formula = formulaL, family = binomial, data = trainData)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -1.246  -1.195   1.109   1.151   1.409  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)   
## (Intercept)  0.03916    0.04623   0.847  0.39699   
## n1           0.18610    0.05755   3.233  0.00122 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 2772.6  on 1999  degrees of freedom
## Residual deviance: 2762.0  on 1998  degrees of freedom
## AIC: 2766
## 
## Number of Fisher Scoring iterations: 3
## 
## [1] "one noise variable, logistic regression Noised 39 train mean deviance 1.99233779252217"

## [1] "one noise variable, logistic regression Noised 39 test mean deviance 2.00622149816528"

## [1] "effects model, jacknifed"
## [1] "one noise variable, logistic regression effects model, jackknifed fit model:"
## 
## Call:
## glm(formula = formulaL, family = binomial, data = trainData)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -1.3619  -1.1570   0.9662   1.1980   1.2169  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)   
## (Intercept) -0.04838    0.04731  -1.023  0.30650   
## n1          -0.06366    0.01954  -3.258  0.00112 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 2772.6  on 1999  degrees of freedom
## Residual deviance: 2761.8  on 1998  degrees of freedom
## AIC: 2765.8
## 
## Number of Fisher Scoring iterations: 4
## 
## [1] "one noise variable, logistic regression jackknifed train mean deviance 1.99219567357296"

## [1] "one noise variable, logistic regression jackknifed test mean deviance 2.00542702505421"

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## [1] "one noise variable, logistic regression AverageManyNoisedModels"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.999   2.000   2.001   2.001   2.001   2.004 
## [1] 0.0008298006
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## [1] "one noise variable, logistic regression JackknifeModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.998   2.000   2.002   2.003   2.005   2.013 
## [1] 0.002991706
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## [1] "one noise variable, logistic regression NaiveModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   3.500   3.882   3.973   3.994   4.151   4.452 
## [1] 0.1903538
## [1] "********"
## [1] "********"
## [1] "one noise variable, logistic regression NoisedModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   2.000   2.002   2.004   2.005   2.007   2.015 
## [1] 0.003271046
## [1] "********"
## [1] "*************************************************************"

one variable, logistic regression

## [1] "*************************************************************"
## [1] "one variable, logistic regression"
## [1] "bSigmaBest 10"
## [1] "naive effects model"
## [1] "one variable, logistic regression naive effects model fit model:"
## 
## Call:
## glm(formula = formulaL, family = binomial, data = trainData)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.1243  -1.1809   0.4704   1.1554   1.5778  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)   0.4731     0.0542    8.73   <2e-16 ***
## x1            3.1777     0.2114   15.03   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 2747.0  on 1999  degrees of freedom
## Residual deviance: 2434.7  on 1998  degrees of freedom
## AIC: 2438.7
## 
## Number of Fisher Scoring iterations: 4
## 
## [1] "one variable, logistic regression naive effects model train mean deviance 1.75629049009229"

## [1] "one variable, logistic regression naive effects model test mean deviance 1.74484448505444"

## [1] "effects model, sigma= 10"
## [1] "one variable, logistic regression effects model, sigma= 10 fit model:"
## 
## Call:
## glm(formula = formulaL, family = binomial, data = trainData)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.0258  -1.1625   0.5245   1.1525   1.6410  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  0.41361    0.05161   8.014 1.11e-15 ***
## x1           3.04056    0.20227  15.032  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 2747.0  on 1999  degrees of freedom
## Residual deviance: 2456.2  on 1998  degrees of freedom
## AIC: 2460.2
## 
## Number of Fisher Scoring iterations: 3
## 
## [1] "one variable, logistic regression Noised 10 train mean deviance 1.7717663587731"

## [1] "one variable, logistic regression Noised 10 test mean deviance 1.76627324921548"

## [1] "effects model, jacknifed"
## [1] "one variable, logistic regression effects model, jackknifed fit model:"
## 
## Call:
## glm(formula = formulaL, family = binomial, data = trainData)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.0811  -1.1892   0.4966   1.1600   1.5642  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  0.45308    0.05326   8.508   <2e-16 ***
## x1           2.99703    0.20478  14.636   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 2747.0  on 1999  degrees of freedom
## Residual deviance: 2460.2  on 1998  degrees of freedom
## AIC: 2464.2
## 
## Number of Fisher Scoring iterations: 4
## 
## [1] "one variable, logistic regression jackknifed train mean deviance 1.77463669725858"

## [1] "one variable, logistic regression jackknifed test mean deviance 1.746225629925"

## [1] "********"
## [1] "one variable, logistic regression AverageManyNoisedModels"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.740   1.762   1.769   1.769   1.778   1.796 
## [1] 0.01251551
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## [1] "one variable, logistic regression JackknifeModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.741   1.763   1.770   1.770   1.780   1.796 
## [1] 0.01237005
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## [1] "********"
## [1] "one variable, logistic regression NaiveModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.739   1.763   1.770   1.771   1.781   1.798 
## [1] 0.01305661
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## [1] "********"
## [1] "one variable, logistic regression NoisedModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.738   1.763   1.772   1.773   1.783   1.834 
## [1] 0.01674702
## [1] "********"
## [1] "*************************************************************"

one variable plus noise variable, logistic regression

## [1] "*************************************************************"
## [1] "one variable plus noise variable, logistic regression"
## [1] "bSigmaBest 18"
## [1] "naive effects model"
## [1] "one variable plus noise variable, logistic regression naive effects model fit model:"
## 
## Call:
## glm(formula = formulaL, family = binomial, data = trainData)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.5658  -0.9120   0.3055   0.8035   2.7112  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  0.68760    0.06161   11.16   <2e-16 ***
## x1           3.18452    0.23641   13.47   <2e-16 ***
## n1           2.45247    0.15572   15.75   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 2747.0  on 1999  degrees of freedom
## Residual deviance: 1990.5  on 1997  degrees of freedom
## AIC: 1996.5
## 
## Number of Fisher Scoring iterations: 6
## 
## [1] "one variable plus noise variable, logistic regression naive effects model train mean deviance 1.43587337720022"

## [1] "one variable plus noise variable, logistic regression naive effects model test mean deviance 3.54303901440774"

## [1] "effects model, sigma= 18"
## [1] "one variable plus noise variable, logistic regression effects model, sigma= 18 fit model:"
## 
## Call:
## glm(formula = formulaL, family = binomial, data = trainData)
## 
## Deviance Residuals: 
##    Min      1Q  Median      3Q     Max  
## -2.111  -1.140   0.505   1.110   1.748  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  0.50496    0.05486   9.205  < 2e-16 ***
## x1           3.24797    0.21623  15.021  < 2e-16 ***
## n1           0.25764    0.07576   3.401 0.000672 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 2747.0  on 1999  degrees of freedom
## Residual deviance: 2445.4  on 1997  degrees of freedom
## AIC: 2451.4
## 
## Number of Fisher Scoring iterations: 3
## 
## [1] "one variable plus noise variable, logistic regression Noised 18 train mean deviance 1.76398528021888"

## [1] "one variable plus noise variable, logistic regression Noised 18 test mean deviance 1.78996190188785"

## [1] "effects model, jacknifed"
## [1] "one variable plus noise variable, logistic regression effects model, jackknifed fit model:"
## 
## Call:
## glm(formula = formulaL, family = binomial, data = trainData)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.2012  -1.1757   0.5026   1.1657   1.5936  
## 
## Coefficients:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  0.42346    0.05493   7.710 1.26e-14 ***
## x1           3.00699    0.20534  14.644  < 2e-16 ***
## n1          -0.05278    0.02435  -2.167   0.0302 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 2747.0  on 1999  degrees of freedom
## Residual deviance: 2455.4  on 1997  degrees of freedom
## AIC: 2461.4
## 
## Number of Fisher Scoring iterations: 4
## 
## [1] "one variable plus noise variable, logistic regression jackknifed train mean deviance 1.77119992923416"

## [1] "one variable plus noise variable, logistic regression jackknifed test mean deviance 1.77521675815884"

## [1] "********"
## [1] "one variable plus noise variable, logistic regression AverageManyNoisedModels"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.742   1.760   1.771   1.772   1.782   1.803 
## [1] 0.01360162
## [1] "********"
## [1] "********"
## [1] "one variable plus noise variable, logistic regression JackknifeModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.733   1.762   1.769   1.770   1.778   1.806 
## [1] 0.01417061
## [1] "********"
## [1] "********"
## [1] "one variable plus noise variable, logistic regression NaiveModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   2.875   3.429   3.539   3.567   3.703   4.151 
## [1] 0.2140362
## [1] "********"
## [1] "********"
## [1] "one variable plus noise variable, logistic regression NoisedModel"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.738   1.787   1.800   1.804   1.821   1.876 
## [1] 0.02458687
## [1] "********"
## [1] "*************************************************************"